Crime Analysis Using R and ArcGIS
Dr. Qiusheng Wu
Introduction
Objectives
- Visualizing spatial and temporal trends of criminal activity
- Analyzing factors that may affect unlawful behavior
Study Area
- San Francisco, California
- Police Department Incidents Data 2003-2018 (Direct download link) (435 MB)
Software Needed
Download R and RStudio
First, you’ll download and set up R and RStudio, a free integrated development environment for R. RStudio helps you work in R by providing a coding platform with access to CRAN, the Comprehensive R Archive Network, which contains thousands of R libraries, a built-in viewer for charts and graphs, and other useful features.
If necessary, download R 3.5.0 or later. Accept all defaults in the installation wizard.
If necessary, download RStudio Desktop. Accept all defaults in the installation wizard.
Install R libraries
if (!require(readr)) install.packages("readr")
if (!require(dplyr)) install.packages("dplyr")
if (!require(DT)) install.packages("DT")
if (!require(ggrepel)) install.packages("ggrepel")
if (!require(leaflet)) install.packages("leaflet")Data Preprocessing
Read the data
Load the data using readr and read_csv().
library(readr)
path <- "/path/to/crime/data/SF_Crime_2003_2018.csv"
df <- read_csv(path)Display Data
Display the data using DT and datatable().
library(DT)
df_sub <- df[1:100,] # display the first 100 rows
df_sub$Time <- as.character(df_sub$Time)
datatable(df_sub, options = list(pageLength = 5,scrollX = '400px'))
sprintf("Number of Rows in Dataframe: %s", format(nrow(df),big.mark = ","))[1] "Number of Rows in Dataframe: 2,215,024"Preprocess Data
The All-Caps text is difficult to read. Let’s force the text in the appropriate columns into proper case.
# str(df)
proper_case <- function(x) {
return (gsub("\\b([A-Z])([A-Z]+)", "\\U\\1\\L\\2" , x, perl=TRUE))
}
library(dplyr)
df <- df %>% mutate(Category = proper_case(Category),
Descript = proper_case(Descript),
PdDistrict = proper_case(PdDistrict),
Resolution = proper_case(Resolution),
Time = as.character(Time))
df_sub <- df[1:100,] # display the first 100 rows
datatable(df_sub, options = list(pageLength = 5,scrollX='400px'))Visualize Data
Crime across space
Display crime incident locations on the map using leaflet. Click icons on the map to show incident details.
library(leaflet)
data <- df[1:10000,] # display the first 10,000 rows
data$popup <- paste("<b>Incident #: </b>", data$IncidntNum, "<br>", "<b>Category: </b>", data$Category,
"<br>", "<b>Description: </b>", data$Descript,
"<br>", "<b>Day of week: </b>", data$DayOfWeek,
"<br>", "<b>Date: </b>", data$Date,
"<br>", "<b>Time: </b>", data$Time,
"<br>", "<b>PD district: </b>", data$PdDistrict,
"<br>", "<b>Resolution: </b>", data$Resolution,
"<br>", "<b>Address: </b>", data$Address,
"<br>", "<b>Longitude: </b>", data$X,
"<br>", "<b>Latitude: </b>", data$Y)
leaflet(data, width = "100%") %>% addTiles() %>%
addTiles(group = "OSM (default)") %>%
addProviderTiles(provider = "Esri.WorldStreetMap",group = "World StreetMap") %>%
addProviderTiles(provider = "Esri.WorldImagery",group = "World Imagery") %>%
# addProviderTiles(provider = "NASAGIBS.ViirsEarthAtNight2012",group = "Nighttime Imagery") %>%
addMarkers(lng = ~X, lat = ~Y, popup = data$popup, clusterOptions = markerClusterOptions()) %>%
addLayersControl(
baseGroups = c("OSM (default)","World StreetMap", "World Imagery"),
options = layersControlOptions(collapsed = FALSE)
)Crime Over Time
library(dplyr)
df_crime_daily <- df %>%
mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
group_by(Date) %>%
summarize(count = n()) %>%
arrange(Date)
library(ggplot2)
library(scales)
plot_daily_crimes <- ggplot(df_crime_daily, aes(x = Date, y = count)) +
geom_line(color = "#F2CA27", size = 0.1) +
geom_smooth(color = "#1A1A1A") +
# fte_theme() +
scale_x_date(breaks = date_breaks("1 year"), labels = date_format("%Y")) +
labs(x = "Date of Crime", y = "Number of Crimes", title = "Daily Crimes in San Francisco (2003 – 2018)")
plot_daily_crimes
Aggregate Data
Summarize the data by incident category.
df_category <- sort(table(df$Category),decreasing = TRUE)
df_category <- data.frame(df_category[df_category > 10000])
colnames(df_category) <- c("Category", "Frequency")
df_category$Percentage <- df_category$Frequency / sum(df_category$Frequency)
datatable(df_category, options = list(scrollX = '400px'))Create a bar plot based on the incident category.
library(ggplot2)
library(ggrepel)
bp<-ggplot(df_category, aes(x=Category, y=Frequency, fill=Category)) + geom_bar(stat="identity") +
theme(axis.text.x=element_blank()) + geom_text_repel(data=df_category, aes(label=Category))
bp
Create a pie chart based on the incident category.
bp<-ggplot(df_category, aes(x="", y=Percentage, fill=Category)) + geom_bar(stat="identity")
pie <- bp + coord_polar("y")
pie
Temporal Trends
Theft Over Time
Create a chart of crimes (Larceny/Theft) over time.
df_theft <- df %>% filter(grepl("Larceny/Theft", Category))
df_theft_daily <- df_theft %>%
mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
group_by(Date) %>%
summarize(count = n()) %>%
arrange(Date)
library(ggplot2)
library(scales)
plot_daily_theft <- ggplot(df_theft_daily, aes(x = Date, y = count)) +
geom_line(color = "#F2CA27", size = 0.1) +
geom_smooth(color = "#1A1A1A") +
# fte_theme() +
scale_x_date(breaks = date_breaks("1 year"), labels = date_format("%Y")) +
labs(x = "Date of Theft", y = "Number of Thefts", title = "Daily Thefts in San Francisco (2003 – 2018)")
plot_daily_theft
Theft Time Heatmap
Aggregate counts of thefts by Day-of-Week and Time to create heat map. Fortunately, the Day-Of-Week part is pre-derived, but Hour is slightly harder.
get_hour <- function(x) {
return(as.numeric(strsplit(x,":")[[1]][1]))
}
df_theft_time <- df_theft %>%
mutate(Hour = sapply(Time, get_hour)) %>%
group_by(DayOfWeek, Hour) %>%
summarize(count = n())
# df_theft_time %>% head(10)
datatable(df_theft_time, options = list(scrollX = '400px'))Reorder and format Factors.
dow_format <- c("Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday")
hour_format <- c(paste(c(12,1:11),"AM"), paste(c(12,1:11),"PM"))
df_theft_time$DayOfWeek <- factor(df_theft_time$DayOfWeek, level = rev(dow_format))
df_theft_time$Hour <- factor(df_theft_time$Hour, level = 0:23, label = hour_format)
# df_theft_time %>% head(10)
datatable(df_theft_time, options = list(scrollX = '400px'))Create Time Heatmap
plot_theft_heatmap <- ggplot(df_theft_time, aes(x = Hour, y = DayOfWeek, fill = count)) +
geom_tile() +
# fte_theme() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.6), legend.title = element_blank(), legend.position = "top", legend.direction = "horizontal", legend.key.width = unit(2, "cm"), legend.key.height = unit(0.25, "cm"), legend.margin = unit(-0.5,"cm"), panel.margin = element_blank()) +
labs(x = "Hour of Theft (Local Time)", y = "Day of Week of Theft", title = "Number of Thefts in San Francisco by Time of Theft (2003-2018)") +
scale_fill_gradient(low = "white", high = "#27AE60", labels = comma)
plot_theft_heatmap
Hmm, why is there a surge at 6-7PM on weekdays?
Arrest Over Time
Create a chart of arrests over time.
df_arrest <- df %>% filter(grepl("Arrest", Resolution))
df_arrest_daily <- df_arrest %>%
mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
group_by(Date) %>%
summarize(count = n()) %>%
arrange(Date)
library(ggplot2)
library(scales)
plot_daily_arrests <- ggplot(df_arrest_daily, aes(x = Date, y = count)) +
geom_line(color = "#F2CA27", size = 0.1) +
geom_smooth(color = "#1A1A1A") +
# fte_theme() +
scale_x_date(breaks = date_breaks("1 year"), labels = date_format("%Y")) +
labs(x = "Date of Arrest", y = "# of Police Arrests", title = "Daily Police Arrests in San Francisco (2003 – 2018)")
plot_daily_arrests
get_hour <- function(x) {
return(as.numeric(strsplit(x,":")[[1]][1]))
}
df_arrest_time <- df_arrest %>%
mutate(Hour = sapply(Time, get_hour)) %>%
group_by(DayOfWeek, Hour) %>%
summarize(count = n())
dow_format <- c("Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday")
hour_format <- c(paste(c(12,1:11),"AM"), paste(c(12,1:11),"PM"))
df_arrest_time$DayOfWeek <- factor(df_arrest_time$DayOfWeek, level = rev(dow_format))
df_arrest_time$Hour <- factor(df_arrest_time$Hour, level = 0:23, label = hour_format)
plot_time_arrest <- ggplot(df_arrest_time, aes(x = Hour, y = DayOfWeek, fill = count)) +
geom_tile() +
# fte_theme() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.6), legend.title = element_blank(), legend.position = "top", legend.direction = "horizontal", legend.key.width = unit(2, "cm"), legend.key.height = unit(0.25, "cm"), legend.margin = unit(-0.5,"cm"), panel.margin = element_blank()) +
labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Number of Police Arrests in San Francisco by Time of Arrest (2003 – 2018)") +
scale_fill_gradient(low = "white", high = "#27AE60", labels = comma)
plot_time_arrest
Hmm, why is there a surge on Wednesday afternoon, and at 4-5PM on all days? Let’s look at subgroups to verify there isn’t a latent factor.
Correlation Analysis
Factor by Crime Category
Certain types of crime may be more time dependent. (e.g., more traffic violations when people leave work)
df_top_crimes <- df_arrest %>%
group_by(Category) %>%
summarize(count = n()) %>%
arrange(desc(count))
datatable(df_top_crimes, options = list(pageLength = 10,scrollX = '400px'))df_arrest_time_crime <- df_arrest %>%
filter(Category %in% df_top_crimes$Category[2:19]) %>%
mutate(Hour = sapply(Time, get_hour)) %>%
group_by(Category, DayOfWeek, Hour) %>%
summarize(count = n())
df_arrest_time_crime$DayOfWeek <- factor(df_arrest_time_crime$DayOfWeek, level = rev(dow_format))
df_arrest_time_crime$Hour <- factor(df_arrest_time_crime$Hour, level = 0:23, label = hour_format)
datatable(df_arrest_time_crime, options = list(pageLength = 10,scrollX = '400px'))plot_num_arrests <- ggplot(df_arrest_time_crime, aes(x = Hour, y = DayOfWeek, fill = count)) +
geom_tile() +
# fte_theme() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Number of Police Arrests in San Francisco by Category and Time of Arrest (2003-2018)") +
scale_fill_gradient(low = "white", high = "#2980B9") +
facet_wrap(~ Category, nrow = 6)
plot_num_arrests
Good, but the gradients aren’t helpful because they are not normalized. We need to normalize the range on each facet. (unfortunately, this makes the value of the gradient unhelpful)
df_arrest_time_crime <- df_arrest_time_crime %>%
group_by(Category) %>%
mutate(norm = count/sum(count))
datatable(df_arrest_time_crime, options = list(pageLength = 10,scrollX = '400px'))plot <- ggplot(df_arrest_time_crime, aes(x = Hour, y = DayOfWeek, fill = norm)) +
geom_tile() +
# fte_theme() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Police Arrests in San Francisco by Time of Arrest, Normalized by Type of Crime (2003-2018)") +
scale_fill_gradient(low = "white", high = "#2980B9") +
facet_wrap(~ Category, nrow = 6)
plot
Factor by Police District
Same as above, but with a different facet.
df_arrest_time_district <- df_arrest %>%
mutate(Hour = sapply(Time, get_hour)) %>%
group_by(PdDistrict, DayOfWeek, Hour) %>%
summarize(count = n()) %>%
group_by(PdDistrict) %>%
mutate(norm = count/sum(count))
df_arrest_time_district$DayOfWeek <- factor(df_arrest_time_district$DayOfWeek, level = rev(dow_format))
df_arrest_time_district$Hour <- factor(df_arrest_time_district$Hour, level = 0:23, label = hour_format)
datatable(df_arrest_time_district, options = list(pageLength = 10,scrollX = '400px'))plot_district <- ggplot(df_arrest_time_district, aes(x = Hour, y = DayOfWeek, fill = norm)) +
geom_tile() +
# fte_theme() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Police Arrests in San Francisco by Time of Arrest, Normalized by Station (2003-2018)") +
scale_fill_gradient(low = "white", high = "#8E44AD") +
facet_wrap(~ PdDistrict, nrow = 5)
plot_district
Factor by Month
If crime is tied to activities, the period at which activies end may impact.
df_arrest_time_month <- df_arrest %>%
mutate(Month = format(as.Date(Date, "%m/%d/%Y"), "%B"), Hour = sapply(Time, get_hour)) %>%
group_by(Month, DayOfWeek, Hour) %>%
summarize(count = n()) %>%
group_by(Month) %>%
mutate(norm = count/sum(count))
df_arrest_time_month$DayOfWeek <- factor(df_arrest_time_month$DayOfWeek, level = rev(dow_format))
df_arrest_time_month$Hour <- factor(df_arrest_time_month$Hour, level = 0:23, label = hour_format)
# Set order of month facets by chronological order instead of alphabetical
df_arrest_time_month$Month <- factor(df_arrest_time_month$Month,
level = c("January","February","March","April","May","June","July","August","September","October","November","December"))
plot_monthly <- ggplot(df_arrest_time_month, aes(x = Hour, y = DayOfWeek, fill = norm)) +
geom_tile() +
# fte_theme() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Police Arrests in San Francisco from 2007 – 2016 by Time of Arrest, Normalized by Month") +
scale_fill_gradient(low = "white", high = "#E74C3C") +
facet_wrap(~ Month, nrow = 4)
plot_monthly
Factor By Year
Perhaps things changed overtime?
df_arrest_time_year <- df_arrest %>%
mutate(Year = format(as.Date(Date, "%m/%d/%Y"), "%Y"), Hour = sapply(Time, get_hour)) %>%
group_by(Year, DayOfWeek, Hour) %>%
summarize(count = n()) %>%
group_by(Year) %>%
mutate(norm = count/sum(count))
df_arrest_time_year$DayOfWeek <- factor(df_arrest_time_year$DayOfWeek, level = rev(dow_format))
df_arrest_time_year$Hour <- factor(df_arrest_time_year$Hour, level = 0:23, label = hour_format)
plot_yearly <- ggplot(df_arrest_time_year, aes(x = Hour, y = DayOfWeek, fill = norm)) +
geom_tile() +
# fte_theme() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Police Arrests in San Francisco from 2007 – 2016 by Time of Arrest, Normalized by Year") +
scale_fill_gradient(low = "white", high = "#E67E22") +
facet_wrap(~ Year, nrow = 6)
plot_yearly
Spatiotemporal Trends
Analyze Crime Hot Spots
Create Space Time Cube By Aggregating Points
Emerging Hot Spot Analysis
Enhance Data Layer
Add additional attributes to the dataset.
Optimized Hot Spot Analysis
Identify areas with unusually high crime rates.
Correlation Analysis
References
---
title: "Crime Analysis Using R and ArcGIS"
author: Dr. Qiusheng Wu
output:
  html_notebook:
    toc: true
    toc_float: true
---

## Introduction
### Objectives
* **Visualizing spatial and temporal trends of criminal activity**
* **Analyzing factors that may affect unlawful behavior**

### Study Area
* San Francisco, California
* [Police Department Incidents Data 2003-2018](https://goo.gl/RB44BM) (Direct download [ link](https://data.sfgov.org/api/views/tmnf-yvry/rows.csv?accessType=DOWNLOAD)) (435 MB)

## Software Needed
### Download R and RStudio
First, you'll download and set up R and RStudio, a free integrated development environment for R. RStudio helps you
work in R by providing a coding platform with access to CRAN, the Comprehensive R Archive Network, which contains
thousands of R libraries, a built-in viewer for charts and graphs, and other useful features.

*  If necessary, download [R 3.5.0 or later](https://cloud.r-project.org/). Accept all defaults in the installation wizard.

*  If necessary, download [RStudio Desktop](https://www.rstudio.com/products/rstudio/download). Accept all defaults in the installation wizard.

### Install R libraries
```{r install libraries, message=FALSE, warning=FALSE}
if (!require(readr)) install.packages("readr")
if (!require(dplyr)) install.packages("dplyr")
if (!require(DT)) install.packages("DT")
if (!require(ggrepel)) install.packages("ggrepel")
if (!require(leaflet)) install.packages("leaflet")
```

## Data Preprocessing
### Read the data
Load the data using readr and read_csv().
```{r, echo=TRUE, message=FALSE, warning=FALSE}
library(readr)
path <- "/path/to/crime/data/SF_Crime_2003_2018.csv"
df <- read_csv(path)
```

### Display Data
Display the data using DT and datatable().
```{r}
library(DT)
df_sub <- df[1:100,]  # display the first 100 rows
df_sub$Time <- as.character(df_sub$Time) 
datatable(df_sub, options = list(pageLength = 5,scrollX = '400px'))

sprintf("Number of Rows in Dataframe: %s", format(nrow(df),big.mark = ","))
```

### Preprocess Data  
The All-Caps text is difficult to read. Let's force the text in the appropriate columns into proper case. 

```{r}
# str(df)
proper_case <- function(x) {
  return(gsub("\\b([A-Z])([A-Z]+)", "\\U\\1\\L\\2" , x, perl = TRUE))
}

library(dplyr)
df <- df %>% mutate(Category = proper_case(Category),
                    Descript = proper_case(Descript),
                    PdDistrict = proper_case(PdDistrict),
                    Resolution = proper_case(Resolution),
                    Time = as.character(Time))
df_sub <- df[1:100,]  # display the first 100 rows
datatable(df_sub, options = list(pageLength = 5,scrollX = '400px'))
```


## Visualize Data
### Crime across space
Display crime incident locations on the map using leaflet. Click icons on the map to show incident details.
```{r}
library(leaflet)

data <- df[1:10000,] # display the first 10,000 rows
data$popup <- paste("<b>Incident #: </b>", data$IncidntNum, "<br>", "<b>Category: </b>", data$Category,
                    "<br>", "<b>Description: </b>", data$Descript,
                    "<br>", "<b>Day of week: </b>", data$DayOfWeek,
                    "<br>", "<b>Date: </b>", data$Date,
                    "<br>", "<b>Time: </b>", data$Time,
                    "<br>", "<b>PD district: </b>", data$PdDistrict,
                    "<br>", "<b>Resolution: </b>", data$Resolution,
                    "<br>", "<b>Address: </b>", data$Address,
                    "<br>", "<b>Longitude: </b>", data$X,
                    "<br>", "<b>Latitude: </b>", data$Y)

leaflet(data, width = "100%") %>% addTiles() %>%
  addTiles(group = "OSM (default)") %>%
  addProviderTiles(provider = "Esri.WorldStreetMap",group = "World StreetMap") %>%
  addProviderTiles(provider = "Esri.WorldImagery",group = "World Imagery") %>%
  # addProviderTiles(provider = "NASAGIBS.ViirsEarthAtNight2012",group = "Nighttime Imagery") %>%
  addMarkers(lng = ~X, lat = ~Y, popup = data$popup, clusterOptions = markerClusterOptions()) %>%
  addLayersControl(
    baseGroups = c("OSM (default)","World StreetMap", "World Imagery"),
    options = layersControlOptions(collapsed = FALSE)
  )
```


### Crime Over Time
```{r, fig.width=10, message=FALSE, warning=FALSE}
library(dplyr)

df_crime_daily <- df %>%
  mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
  group_by(Date) %>%
  summarize(count = n()) %>%
  arrange(Date)

library(ggplot2)
library(scales)
plot_daily_crimes <- ggplot(df_crime_daily, aes(x = Date, y = count)) +
  geom_line(color = "#F2CA27", size = 0.1) +
  geom_smooth(color = "#1A1A1A") +
  # fte_theme() +
  scale_x_date(breaks = date_breaks("1 year"), labels = date_format("%Y")) +
  labs(x = "Date of Crime", y = "Number of Crimes", title = "Daily Crimes in San Francisco (2003–2018)")
plot_daily_crimes
```


## Aggregate Data
Summarize the data by incident category.
```{r}
df_category <- sort(table(df$Category),decreasing = TRUE)
df_category <- data.frame(df_category[df_category > 10000])
colnames(df_category) <- c("Category", "Frequency")
df_category$Percentage <- df_category$Frequency / sum(df_category$Frequency)
datatable(df_category, options = list(scrollX = '400px'))
```


Create a bar plot based on the incident category.
```{r fig.width=10}
library(ggplot2)
library(ggrepel)
bp<-ggplot(df_category, aes(x=Category, y=Frequency, fill=Category)) + geom_bar(stat="identity") + 
  theme(axis.text.x=element_blank()) + geom_text_repel(data=df_category, aes(label=Category))
bp
```

Create a pie chart based on the incident category.
```{r fig.width=10}
bp<-ggplot(df_category, aes(x="", y=Percentage, fill=Category)) + geom_bar(stat="identity") 
pie <- bp + coord_polar("y") 
pie
```


## Temporal Trends 

### Theft Over Time
Create a chart of crimes (Larceny/Theft) over time.
```{r, message=FALSE, warning=FALSE, fig.width=10}
df_theft <- df %>% filter(grepl("Larceny/Theft", Category))

df_theft_daily <- df_theft %>%
  mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
  group_by(Date) %>%
  summarize(count = n()) %>%
  arrange(Date)

library(ggplot2)
library(scales)
plot_daily_theft <- ggplot(df_theft_daily, aes(x = Date, y = count)) +
  geom_line(color = "#F2CA27", size = 0.1) +
  geom_smooth(color = "#1A1A1A") +
  # fte_theme() +
  scale_x_date(breaks = date_breaks("1 year"), labels = date_format("%Y")) +
  labs(x = "Date of Theft", y = "Number of Thefts", title = "Daily Thefts in San Francisco (2003–2018)")
plot_daily_theft
```

### Theft Time Heatmap
Aggregate counts of thefts by Day-of-Week and Time to create heat map. Fortunately, the Day-Of-Week part is pre-derived, but Hour is slightly harder.
```{r}
get_hour <- function(x) {
  return(as.numeric(strsplit(x,":")[[1]][1]))
}

df_theft_time <- df_theft %>%
  mutate(Hour = sapply(Time, get_hour)) %>%
  group_by(DayOfWeek, Hour) %>%
  summarize(count = n())
# df_theft_time %>% head(10)
datatable(df_theft_time, options = list(scrollX = '400px'))
```

Reorder and format Factors.
```{r}
dow_format <- c("Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday")
hour_format <- c(paste(c(12,1:11),"AM"), paste(c(12,1:11),"PM"))

df_theft_time$DayOfWeek <- factor(df_theft_time$DayOfWeek, level = rev(dow_format))
df_theft_time$Hour <- factor(df_theft_time$Hour, level = 0:23, label = hour_format)

# df_theft_time %>% head(10)
datatable(df_theft_time, options = list(scrollX = '400px'))
```

Create Time Heatmap
```{r, message=FALSE, warning=FALSE, fig.width=10}
plot_theft_heatmap <- ggplot(df_theft_time, aes(x = Hour, y = DayOfWeek, fill = count)) +
  geom_tile() +
  # fte_theme() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.6), legend.title = element_blank(), legend.position = "top", legend.direction = "horizontal", legend.key.width = unit(2, "cm"), legend.key.height = unit(0.25, "cm"), legend.margin = unit(-0.5,"cm"), panel.margin = element_blank()) +
  labs(x = "Hour of Theft (Local Time)", y = "Day of Week of Theft", title = "Number of Thefts in San Francisco by Time of Theft (2003-2018)") +
  scale_fill_gradient(low = "white", high = "#27AE60", labels = comma)
plot_theft_heatmap
```

Hmm, why is there a surge at 6-7PM on weekdays? 

### Arrest Over Time
Create a chart of arrests over time.
```{r, message=FALSE, warning=FALSE, fig.width=10}
df_arrest <- df %>% filter(grepl("Arrest", Resolution))

df_arrest_daily <- df_arrest %>%
  mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
  group_by(Date) %>%
  summarize(count = n()) %>%
  arrange(Date)

library(ggplot2)
library(scales)
plot_daily_arrests <- ggplot(df_arrest_daily, aes(x = Date, y = count)) +
  geom_line(color = "#F2CA27", size = 0.1) +
  geom_smooth(color = "#1A1A1A") +
  # fte_theme() +
  scale_x_date(breaks = date_breaks("1 year"), labels = date_format("%Y")) +
  labs(x = "Date of Arrest", y = "# of Police Arrests", title = "Daily Police Arrests in San Francisco (2003–2018)")
plot_daily_arrests
```

```{r, fig.width=10, message=FALSE, warning=FALSE}
get_hour <- function(x) {
  return(as.numeric(strsplit(x,":")[[1]][1]))
}

df_arrest_time <- df_arrest %>%
  mutate(Hour = sapply(Time, get_hour)) %>%
  group_by(DayOfWeek, Hour) %>%
  summarize(count = n())

dow_format <- c("Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday")
hour_format <- c(paste(c(12,1:11),"AM"), paste(c(12,1:11),"PM"))

df_arrest_time$DayOfWeek <- factor(df_arrest_time$DayOfWeek, level = rev(dow_format))
df_arrest_time$Hour <- factor(df_arrest_time$Hour, level = 0:23, label = hour_format)

plot_time_arrest <- ggplot(df_arrest_time, aes(x = Hour, y = DayOfWeek, fill = count)) +
  geom_tile() +
  # fte_theme() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.6), legend.title = element_blank(), legend.position = "top", legend.direction = "horizontal", legend.key.width = unit(2, "cm"), legend.key.height = unit(0.25, "cm"), legend.margin = unit(-0.5,"cm"), panel.margin = element_blank()) +
  labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Number of Police Arrests in San Francisco by Time of Arrest (2003–2018)") +
  scale_fill_gradient(low = "white", high = "#27AE60", labels = comma)
plot_time_arrest
```
Hmm, why is there a surge on Wednesday afternoon, and at 4-5PM on all days? Let's look at subgroups to verify there isn't a latent factor.

## Correlation Analysis

### Factor by Crime Category
Certain types of crime may be more time dependent. (e.g., more traffic violations when people leave work)
```{r}
df_top_crimes <- df_arrest %>%
  group_by(Category) %>% 
  summarize(count = n()) %>%
  arrange(desc(count))

datatable(df_top_crimes, options = list(pageLength = 10,scrollX = '400px'))
```

```{r}
df_arrest_time_crime <- df_arrest %>%
  filter(Category %in% df_top_crimes$Category[2:19]) %>%
  mutate(Hour = sapply(Time, get_hour)) %>%
  group_by(Category, DayOfWeek, Hour) %>% 
  summarize(count = n())

df_arrest_time_crime$DayOfWeek <- factor(df_arrest_time_crime$DayOfWeek, level = rev(dow_format))
df_arrest_time_crime$Hour <- factor(df_arrest_time_crime$Hour, level = 0:23, label = hour_format)

datatable(df_arrest_time_crime, options = list(pageLength = 10,scrollX = '400px'))
```

```{r, fig.height=10, fig.width=10, message=FALSE, warning=FALSE}
plot_num_arrests <- ggplot(df_arrest_time_crime, aes(x = Hour, y = DayOfWeek, fill = count)) +
  geom_tile() +
  # fte_theme() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
  labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Number of Police Arrests in San Francisco by Category and Time of Arrest (2003-2018)") +
  scale_fill_gradient(low = "white", high = "#2980B9") +
  facet_wrap(~ Category, nrow = 6)
plot_num_arrests
```

Good, but the gradients aren't helpful because they are not normalized. We need to normalize the range on each facet. (unfortunately, this makes the value of the gradient unhelpful)

```{r}
df_arrest_time_crime <- df_arrest_time_crime %>%
  group_by(Category) %>%
  mutate(norm = count/sum(count))

datatable(df_arrest_time_crime, options = list(pageLength = 10,scrollX = '400px'))

```

```{r, fig.height=10, fig.width=10, message=FALSE, warning=FALSE}
plot <- ggplot(df_arrest_time_crime, aes(x = Hour, y = DayOfWeek, fill = norm)) +
  geom_tile() +
  # fte_theme() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
  labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Police Arrests in San Francisco by Time of Arrest, Normalized by Type of Crime (2003-2018)") +
  scale_fill_gradient(low = "white", high = "#2980B9") +
  facet_wrap(~ Category, nrow = 6)
plot
```

### Factor by Police District
Same as above, but with a different facet.

```{r}
df_arrest_time_district <- df_arrest %>%
  mutate(Hour = sapply(Time, get_hour)) %>%
  group_by(PdDistrict, DayOfWeek, Hour) %>% 
  summarize(count = n()) %>%
  group_by(PdDistrict) %>%
  mutate(norm = count/sum(count))

df_arrest_time_district$DayOfWeek <- factor(df_arrest_time_district$DayOfWeek, level = rev(dow_format))
df_arrest_time_district$Hour <- factor(df_arrest_time_district$Hour, level = 0:23, label = hour_format)

datatable(df_arrest_time_district, options = list(pageLength = 10,scrollX = '400px'))

```

```{r, fig.height=10, fig.width=10, message=FALSE, warning=FALSE}
plot_district <- ggplot(df_arrest_time_district, aes(x = Hour, y = DayOfWeek, fill = norm)) +
  geom_tile() +
  # fte_theme() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
  labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Police Arrests in San Francisco by Time of Arrest, Normalized by Station (2003-2018)") +
  scale_fill_gradient(low = "white", high = "#8E44AD") +
  facet_wrap(~ PdDistrict, nrow = 5)
plot_district
```

### Factor by Month

If crime is tied to activities, the period at which activies end may impact.
```{r, fig.height=8, fig.width=10, message=FALSE, warning=FALSE}
df_arrest_time_month <- df_arrest %>%
  mutate(Month = format(as.Date(Date, "%m/%d/%Y"), "%B"), Hour = sapply(Time, get_hour)) %>%
  group_by(Month, DayOfWeek, Hour) %>% 
  summarize(count = n()) %>%
  group_by(Month) %>%
  mutate(norm = count/sum(count))

df_arrest_time_month$DayOfWeek <- factor(df_arrest_time_month$DayOfWeek, level = rev(dow_format))
df_arrest_time_month$Hour <- factor(df_arrest_time_month$Hour, level = 0:23, label = hour_format)

# Set order of month facets by chronological order instead of alphabetical
df_arrest_time_month$Month <- factor(df_arrest_time_month$Month,
                                     level = c("January","February","March","April","May","June","July","August","September","October","November","December"))

plot_monthly <- ggplot(df_arrest_time_month, aes(x = Hour, y = DayOfWeek, fill = norm)) +
  geom_tile() +
  # fte_theme() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
  labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Police Arrests in San Francisco from 2003–2018 by Time of Arrest, Normalized by Month") +
  scale_fill_gradient(low = "white", high = "#E74C3C") +
  facet_wrap(~ Month, nrow = 4)
plot_monthly
```

### Factor By Year

Perhaps things changed overtime?
```{r, fig.height=10, fig.width=10, message=FALSE, warning=FALSE}
df_arrest_time_year <- df_arrest %>%
  mutate(Year = format(as.Date(Date, "%m/%d/%Y"), "%Y"), Hour = sapply(Time, get_hour)) %>%
  group_by(Year, DayOfWeek, Hour) %>% 
  summarize(count = n()) %>%
  group_by(Year) %>%
  mutate(norm = count/sum(count))

df_arrest_time_year$DayOfWeek <- factor(df_arrest_time_year$DayOfWeek, level = rev(dow_format))
df_arrest_time_year$Hour <- factor(df_arrest_time_year$Hour, level = 0:23, label = hour_format)

plot_yearly <- ggplot(df_arrest_time_year, aes(x = Hour, y = DayOfWeek, fill = norm)) +
  geom_tile() +
  # fte_theme() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.6, size = 4)) +
  labs(x = "Hour of Arrest (Local Time)", y = "Day of Week of Arrest", title = "Police Arrests in San Francisco by Time of Arrest, Normalized by Year (2003-2018)") +
  scale_fill_gradient(low = "white", high = "#E67E22") +
  facet_wrap(~ Year, nrow = 6)
plot_yearly
```

## Spatiotemporal Trends
### Analyze Crime Hot Spots

* Create Space Time Cube By Aggregating Points

* Emerging Hot Spot Analysis

![](https://i.imgur.com/mbFescW.png)

![](https://i.imgur.com/HC9B9Up.png)


### Enhance Data Layer
Add additional attributes to the dataset.

![](https://i.imgur.com/jsEYR3N.png)

### Optimized Hot Spot Analysis
Identify areas with unusually high crime rates.

![](https://i.imgur.com/cTlgnHP.png)

### Correlation Analysis
![](https://i.imgur.com/XKvC7PM.png)

## References

* [San Francisco Crime Data](https://data.sfgov.org/Public-Safety/Police-Department-Incident-Reports-Historical-2003/tmnf-yvry/data)

* [Analyzing  SF Crime Data](http://minimaxir.com/2015/12/sf-arrests/)

* [Analyze Crime Using Statistics and the R-ArcGIS Bridge](http://learn.arcgis.com/en/projects/analyze-crime-using-statistics-and-the-r-arcgis-bridge/)
